Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.
Learn more about Flink at https://flink.apache.org/
This packaging allows you to write Flink programs in Python, but it is currently a very initial version and will change in future versions.
In this initial version only Table API is supported, you can find the documentation at https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/tableApi.html
The tutorial can be found at https://ci.apache.org/projects/flink/flink-docs-stable/tutorials/python_table_api.html
The auto-generated Python docs can be found at https://ci.apache.org/projects/flink/flink-docs-stable/api/python/
Apache Flink Python API depends on Py4J (currently version 0.10.8.1), CloudPickle (currently version 1.2.2), python-dateutil(currently version 2.8.0) and Apache Beam (currently version 2.15.0).
Protocol buffer is used in file flink_fn_execution_pb2.py and the file is generated from flink-fn-execution.proto. Whenever flink-fn-execution.proto is updated, please re-generate flink_fn_execution_pb2.py by executing:
python pyflink/gen_protos.py
PyFlink depends on the following libraries to execute the above script:
- grpcio-tools (>=1.3.5,<=1.14.2)
- setuptools (>=37.0.0)
- pip (>=7.1.0)
Currently, we use conda and tox to verify the compatibility of the Flink Python API for multiple versions of Python and will integrate some useful plugins with tox, such as flake8. We can enter the directory where this README.md file is located and run test cases by executing
./dev/lint-python.sh